The document discusses genome-wide association studies (GWAS) and their significance in identifying genetic associations with diseases and traits. It highlights the consistency of associations between certain gene polymorphisms and various conditions, alongside practical considerations for study design, such as sample size and population stratification. Additionally, it mentions technological advancements in genotyping and the implications of p-values in statistical analyses of genetic data.

1. Genome-wide
Association
Studies
EPI 519
21 October 2010
Joshua C. Bis, PhD
University of Washington, Cardiovascular
The Type 1 Diabetes Genetics Consortium. Nature Genetics, 2009 May 10
Health Research Unit

3. Complex phenotypes
Manolio et al. J. Clin. Invest. 118:1590-1605 (2008).

4. rationale for association studies
Balding. Nature Reviews Genetics. 2006; 7:781-791

5. candidate genes
Manolio, Boerwinkle, O’Donnell, Wilson. Arterioscler Thromb Vasc Biol. 2004;24:1567-1577.

6. highly consistent associations*
Trait Gene Polymorphism Frequency
Deep Vein F5 Arg506Gln 0.015
Thrombosis
Graves’ disease CTLA4 Thr17Ala 0.62
Type 1 diabetes INS 5’VNTR 0.67
HIV infection CCR5 32 bp Ins/Del 0.05-0.07
Alzheimer’s disease APOE Epsilon 2/3/4 0.16-0.24
Creutzfelt-Jakob PRNP Met129Val 0.37
* Associations between polymorphisms and disease where at least 75% of identified studies
achieved statistical significance. (out of 600 gene–disease studies reviewed)
Hirschhorn: Genet Med, Volume 4(2).March/April 2002.45-61

7. “genomics”
The field within genetics
concerned with the structure and
function of the entire DNA
sequence of an individual or
population.
-- Thomas Roderick
McDonald’s Raw Bar
1986

8. genome-wide association study
“… a study of common genetic
variation across the entire
human genome designed to
identify genetic associations with
observable traits.”
-- National Institutes of Health,
“Policy for sharing of data obtained in
NIH-sponsored or conducted GWAS”

9. “A major strength of the
genome-wide approach … has
been its freedom from reliance
on prior knowledge.”
-- “A HapMap harvest of insights into the genetics of
common disease”
(Manolio, Brooks, Collins.)

10. genome-wide publication epidemic
genome.gov/GWAStudies :: 2 November 2009

11. Costs per Genotype
$1.00
ABI
TaqMan
ABI
SNPplex
Illumina
$0.10 GoldenGate Affymetrix
MegAllele
Affymetrix
10K Illumina
Infinium/Sentrix
Perlegen
$0.01 Affymetrix
100k/500K Illumina
2.5M
# SNPs 1 10 100 103 104 105 106
2001 2005
S. Chanock, NCI

12. Modified from http://www.ncbi.nlm.nih.gov/books/bv.fcgi?rid=helpsnpfaq

13. haplotypes
The International HapMap Consortium. Nature | Vol 437 | 27October2005

14. “… to create a public, genome-
wide database of common
human sequence variation,
providing information needed as
a guide to genetic studies of
clinical phenotypes.”
-- October 2002

15. Ben Fry, for Genome Research. November 2005

16. imputation
Use patterns of variation from HapMap to impute genotypes.
Increases power by allowing for association testing at
untyped markers and allows comparisons across studies and
platforms by using a common set of SNPs.
Li, Willer, Sanna, Abecasis. Annu Rev Genomics Hum Genet. 2009;10:387-406

17. (SOME PRACTICAL CONSIDERATIONS)

18. genotyping

19. genotyping

20. genotyping: raw data
Ratio of intensities
from two channels
Calls = 2461
No calls = 27

21. analysis × 2.5 million

22. association study
controls cases
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23. Manhattan plot
(McCarthy et al.,Nature Reviews Genetics, May 2008)

24. p-value
the probability of seeing your data or more extreme
data if the null hypothesis is true.
By chance, with 1,000,000 statistical tests:
• a threshold of p=0.05
would show 50,000 “significant” associations
360 cases : 360 controls
• a threshold of p = 0.05/1,000,000 (5 x 10-8)
would show 0.05 “significant” associations
1590 cases: 1590 controls.

25. study design considerations
Case-control or cohort
Sample size
Phenotype definition
Comparability of cases and controls
• Genotyping quality
• Population substructure
• Laboratory procedures, genotyping, data cleaning

26. population stratification
requires both allele frequency
and disease prevalence differences
Balding. Nature Reviews Genetics. 2006; 7:781-791

27. Q-Q plots
(modified from McCarthy et al.,Nature Reviews Genetics, May 2008)

28. Allele frequency & effect size
Feasibility of identifying genetic variants by risk allele
frequency and strength of genetic effect (odds ratio).
TA Manolio et al. Nature 461, 747-753 (2009) doi:10.1038/nature08494

29. reasons for larger sample size:
• More genotypes / tests • Lower effect size
• More genotype error or • Lower frequency of risk
misclassification allele
• Higher heterogeneity of • Lower correlation
association between marker allele
and risk allele.

30. power & sample size
(Rice, personal communication)

31. Multi-stage discovery
Carry-forward a large number of
potential associations through
multiple, narrowing stages.
Protect against false positives via
replication
Minimize false negative results
via permissive early thresholds
From
Hoover,
R.
Epidemiology.
18(1):13-­‐17,
January
2007.

32. Meta analysis
Combine results from several
studies to increase power using
traditional methods of meta-
analysis.
Allows for first stage discovery of
small effect sizes

33. (SELECTED EXAMPLES)

34. Wellcome Trust Case Control Consortium
Biggest projects undertaken to identify genetic variation that
may be associated with disease
£ 9 million in funding from Wellcome Trust
GWAS of seven common diseases: 2,000 cases each and 3,000
shared controls
All genotyping data available to scientific community
www.wtccc.org.uk; (Nature, vol 447, 7 June 2007)

35. Lon Cardon, SISG 2007

36. UK Control groups are NOT very
different
Lon Cardon, SISG 2007

37. WTCCC Results

38. WTCCC results
Samani, NEJM 2007

39. WTCCC results

40. Coronary Disease GWAS: 9p21
author McPherson Helgadottir Samani Larson
where Science Science NEJM BMC Med Gen
when May 2007 May 2007 August 2007 Sept 2007
design 3-stage case-control case control cohort
case control
discovery OHS 1 deCODE: Iceland A WTCCC Framingham Heart
OHS 2 Study
ARIC
replication CCHS Iceland B German Family Study
DHS 3 U.S. case-control
OHS-3
case severe premature CHD MI MI or revascularization + incident MI
definition fhx of CAD
age at onset <60 <70 males <66
<75 females

41. 9p21 results
study SNP locus hazard/odds ratio PAR
ARIC rs10757274 9p21 AB: 1.18 (1.02-1.37) 12-15%
BB: 1.29 (1.09-1.52)
CCHS rs10757274 9p21 AB: 1.26 (1.12-1.42) 10-13%
BB: 1.38 (1.19-1.60)
deCODE rs10757278 9p21 AB: 1.26 (1.16-1.36) 21%
BB: 1.64 (1.47-1.82)
deCODE rs10757278 9p21 AB: 1.49 (1.31-1.69) 31%
early onset BB: 2.02 (1.72 - 2.36)
Helgadottir, Science 2007
McPherson, Science 2007

42. 9p21 Gene Region

43. 9p21 locus
not located within a “gene”
region contains CDKN2A and CDKN2B genes
• role in cell proliferation, cell aging and apoptosis -
important features of atherogenesis
• Sequencing did not reveal obvious candidates
may implicate a previously unrecognized gene or regulatory
element
same region also associated with type 2 diabetes

44. MIGen: Population
Study Cases (mean age) Controls (mean age)
Italian ATVB (Italy) 1,693 1,668
(39 y) (39 y)
Heart Attack Risk in Puget Sound (USA) 505 559
(46 y) (45 y)
REGICOR (Spain) 312 317
(46 y) (46 y)
MGH (USA) 204 260
(47 y) (54 y)
FINRISK (Finland) 167 172
(47 y) (47 y)
Malmö Diet & Cancer (Sweden) 86 99
(47 y) (49 y)

45. MIGen: Design

46. MIGen: Early MI SNPs
Locus genes of interest
✔ 1p13 CELSR2-PSRC1-SORT1
✔ 1q41 MIA3
✘ 2p36 --
✪ 2q33 WDR12
✪ 3q22.3 MRAS
✪ 6p24.1 PHACTR1
✘ 6q25 MTHFD1L
✔ 9p21 CDKN2A-CDKN2B
✔ 10q11 CXCL12
✘ 15q22 SMAD3
✪ 19p13.2 LDLR
✪ 21q22 MRPS6-SLC5A3-KCNE2

47. MIGen: Risk Score
4 replicated loci:
CDKN2A-B, CELSR2-PSRC1-SORT1, MIA3, CXCL12
5 new loci:
SLC5A3-MRPS6-KCNE2, PHACTR, WDR12, LDLR, PCSK9

48. (LESSONS, QUESTIONS, DIRECTIONS)

49. Published Genome-Wide Associations through 6/2010,
904 published GWA at p<5x10-8 for 165 traits
NHGRI GWA Catalog
www.genome.gov/GWAStudies

50. new biology: genomic context
Manolio, NEJM 2010

51. new biology: mechanisms
Manolio, NEJM 2010

52. new biology: connections
Manolio, NEJM 2010

53. missing heritability (2009)
% of heritability
number of loci explained
Age-related macular degeneration 5 50%
Crohn’s disease 32 20%
Type-2 diabetes 18 6%
HDL cholesterol 7 5%
Height 40 5%
Early-onset MI 9 2.8%
Fasting glucose 4 1.5%
Manolio, Nature 2009

54. missing heritability
many variants with small effects yet to be found
• larger sample sizes have revealed more loci
true positives below significance threshold
contribution of rare variants
failure to identify true causal variant
structural variants poorly captured by arrays
previous estimates of heritability flawed
GxG or GxE interactions

55. missing heritability (update)
Meta-analysis of > 100,000 discovers 59 new associations
SNPs explain ~12% of trait variability & ~ 25% heritability
Some predict MI risk; point to LDL/HDL differences

56. disease prediction
hope: highly predictive and affordable genetic tests
reality: low discriminatory and predictive ability Manolio, NEJM 2010

57. next steps
Ever larger sample sizes
Studies of non-European ethnic populations
Sequencing implicated genetic regions
More complex genetic models
• Gene x Gene interactions
• pooling of rare variants
Functional biology: work in basic science and animal models

58. summary
GWAS have led to new Don’t forget:
biology • case definition
Small effect sizes • QC measures
Not useful in prediction • sample size and power
Much yet to be discovered • multiple testing
More complicated than we • independent replication
thought

59. “There have been few, if any,
similar bursts of discovery in the
history of medical research”
-- “Drinking from the fire hose …” (Hunter & Knox)

60. Consumer Genotyping Toys

61. Consumer Genotyping Toys

62. Consumer Genotyping Toys

63. Consumer Genotyping Toys

64. Sources / References / Reading
1. The International HapMap Consortium.* A haplotype map of the human genome. Nature, 2005. 437(7063): p. 1299-320.[16255080].
2. The Type 1 Diabetes Genetics Consortium.* Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nature Genetics, 2009
May 10 [19430480]
3. Myocardial Infarction Genetics Consortium.* Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number
variants. Nat Genet. 2009 Mar;41(3):334-41 [19198609]
4. The Wellcome Trust Case Control Constortium.* Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 2007. 447
(7145): p. 661-78.[17554300].
5. Balding, D.J., A tutorial on statistical methods for population association studies. Nat Rev Genet, 2006. 7(10): p. 781-91.[16983374].
6. Christensen, K. and J.C. Murray, What genome-wide association studies can do for medicine. N Engl J Med, 2007. 356(11): p. 1094-7.[17360987].
7. Frazer, K.A., et al., A second generation human haplotype map of over 3.1 million SNPs. Nature, 2007. 449(7164): p. 851-61.[17943122].
8. Hirschhorn, J.N., et al., A comprehensive review of genetic association studies. Genet Med, 2002. 4(2): p. 45-61.[11882781].
9. Hoover, R. The evolution of epidemiologic research: from cottage industry to "big" science. Epidemiology. 2007 Jan;18(1):13-7. [17179754]
10. Hunter, D.J. and P. Kraft, Drinking from the fire hose--statistical issues in genomewide association studies. N Engl J Med, 2007. 357(5): p. 436-9.[17634446].
11. Li Y, Willer C, Sanna S, Abecasis G., Genotype imputation. Annu Rev Genomics Hum Genet. 2009;10:387-406. [19715440]
12. Johnson AD and O’Donnell CJ: Open access database of GWA results, BMC Medical Genetics 2009: 10:6
13. Manolio, T.A., et al., Genetics of ultrasonographic carotid atherosclerosis. Arterioscler Thromb Vasc Biol, 2004. 24(9): p. 1567-77.[15256397].
14. Manolio, T.A., L.D. Brooks, and F.S. Collins, A HapMap harvest of insights into the genetics of common disease. J Clin Invest, 2008. 118(5): p. 1590-605.[18451988].
15. Manolio TA, Collins FS, Cox NJ, et al. Finding the missing heritability of complex diseases. Nature. 2009 Oct 8;461(7265):747-53. [19812666]
16. Manolio, TA. Genomewide association studies and assessment of the risk of disease. N Engl J Med. 2010 Jul 8;363(2):166-76. [20647212]
17. McCarthy, M.I., et al., Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet, 2008. 9(5): p. 356-69.[18398418].
18. Pearson, T.A. and T.A. Manolio, How to interpret a genome-wide association study. JAMA, 2008. 299(11): p. 1335-44.[18349094].
19. Samani NJ, Erdmann J, Hall AS, et al. Genomewide association analysis of coronary artery disease. N Engl J Med. 2007 Aug 2;357(5):443-53. [17634449]
20. Teslovich TM, Musunuru K, Smith AV, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010 Aug 5;466(7307):707-13. [20686565]
21. NHGRI catalog of published GWA studies (http://genome.gov/GWASstudies)